Follow the IMRAD standard scientific structure: - Introduction - Materials and Methods - Results (And) - Discussion With a technical focus, but minding to communicate which-ever biological insights you arrived at

Should not include all your code (we will look into that at the individual examinations), but rather focus on the broader picture of what you did and include data summaries and visualisations

Created using ioslides_presentation rmarkdown (i.e. the right-most doc column in the project organisation will be a rmarkdown based presentation)

• Intro to snake venom
• Data set for the study:
  • Venom compositions from snakes all around the world
• Goal of study:
  • Group snakes by genus based on venom composition (PCA, K-means, ANN)

• Main data
• New data, adding three snakes from litterature

• Talk about how the data sets are merged
• Talk about which methods have been used to reach the goal for the study (PCA, K-means, ANN)

• Show dirty data vs. clean data
• Show region, family, genus, species rows
• Show grouping of the toxin families columns

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   Snake = col_character(),
##   Genus = col_character(),
##   Species = col_character(),
##   Reference = col_character(),
##   Country = col_character(),
##   Continent = col_character(),
##   Family = col_character()
## )

## See spec(...) for full column specifications.

• Prediction of snake family
  • Vanilla ANN with 4 hidden neurons achived 100 % test accuracy
• Prediction of which continent, the snake originated from
  • Attempted a number of architechtures ranging from 1 to 4 hidden layers, with/without dropout and tried optimizing hyperparameters.
  • Problems with overfitting - further regularization e.g. early stopping might be a solution
  • Question might be ill posed - venom composition predicts snake family and not necessarily location. e.g. snakes from two different families both from the same country have completely different venom compositions.